AZoLifeSciences on MSN

Machine learning enhances manufacturing of complex protein drugs

Industrial yeasts are a powerhouse of protein production, used to manufacture vaccines, biopharmaceuticals, and other useful compounds.

AI model learns yeast DNA 'language' to boost protein drug output

Industrial yeasts are a powerhouse of protein production, used to manufacture vaccines, biopharmaceuticals, and other useful ...
IEEE

Encoder-Decoder Based Deep Reinforcement Learning for Multi-AUV Assisted Data Collection in UWSNs

Abstract: Reliable and timely data collection poses a significant challenge for underwater wireless sensor networks (UWSNs), primarily due to the extremely low data rate of underwater communication ...
GitHub

utrobinmv/dinov3-finetune_experiments

This repository explores finetuning DINOv3 (Siméoni et al., 2025) or DINOv2 (Oquab et al., 2024) encoder weights using Low-Rank Adaptation (Hu et al., 2021) (LoRA) and a simple 1x1 convolution decoder ...
GitHub

GAOT: Geometry-Aware Operator Transformer

Learning solution operators of PDEs on arbitrary domains accurately and efficiently is a critical yet challenging task in engineering and industrial simulations. While numerous operator learning ...
Scientific Research Publishing

Feng, L., Zhao, C. and Sun, Y. (2021) Dual Attention-Based Encoder-Decoder: A Customized Sequence-to-Sequence Learning for Soft Sensor Development. IEEE Transactions on Neural ...

ABSTRACT: In this paper, a novel multilingual OCR (Optical Character Recognition) method for scanned papers is provided. Current open-source solutions, like Tesseract, offer extremely high accuracy ...
cc.gatech.edu

Machine Learning Encoder Improves Weather Forecasting and Tsunami Prediction

Successful test results of a new machine learning (ML) technique developed at Georgia Tech could help communities prepare for extreme weather and coastal flooding. The approach could also be applied ...
Georgia Tech News Center

Machine Learning Encoder Improves Weather Forecasting and Tsunami Prediction

Ph.D. student Phillip Si and Assistant Professor Peng Chen developed Latent-EnSF, a technique that improves how ML models assimilate data to make predictions.